Abstract

The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.

title = "Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material",

abstract = "The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.",

N2 - The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.

AB - The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.